.png){kind=icon}
返回
Model Updating Method for Jacket Platform Considering Different Component Degradation Based on Deep Learning
.png){kind=icon}
PRE
AI
DOI：10.1142/S0219455424500706.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Marine platforms are located in complex environments, and safety deteriorates throughout the day. It is necessary to analyze the jacket platform structure by the finite element method. Problems such as platform structure variation and fatigue corrosion lead to model deviation. In this paper, a finite element model correction method based on deep learning is proposed with a jacket platform as the engineering background. First, different platform design parameters are selected, and the corresponding fundamental frequencies are obtained by finite element simulation. Second, the input features are extended as necessary to increase the damage-sensitive information, with the nonlinear differences between the two reduced by an improved ResNet50 network. Finally, the correction values of the finite element model are obtained by combining the measured data with the inherent structural frequencies obtained by covariance-driven stochastic subspace identification (Cov-SSI). The results show that the error after correction is less than 4%, which can reflect the real marine platform state well.
Keyword:
Model updating
marine platforms
deep learning
inherent frequencies
期刊
IF:
3.4
论文数:
3.1K
被引数:
6.3K
机构
引用论文
Time relevance, citation of reporting guidelines, and breadth of literature search in systematic reviews in orthodontics时效性、报告指南的引用以及正畸领域系统评价中的文献检索广度
OAAI
OAAI分享收藏Comparative study of layer by layer assembled multilayer films based on graphene oxide and reduced graphene oxide on flexible polyurethane foam: flame retardant and smoke suppression properties.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏A Deep Learning-Based Method for Automatic Abnormal Data Detection: Case Study for Bridge Structural Health Monitoring基于深度学习的异常数据自动检测方法 -- 以桥梁结构健康监测为例
PREAI
分享收藏The UK Research Excellence Framework and the Matthew effect: Insights from machine learning英国卓越研究框架与马太效应：来自机器学习的见解PLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏Hierarchical model updating strategy of complex assembled structures with uncorrelated dynamic modesOAAI
OAAI分享收藏Let's Explore! The Factor, Reliability, and Validity Analyses of Readiness for a Knowledge-Based Economy Among Undergraduate Students让我们探索！关于本科生对知识经济准备度的因子、信度和效度分析
OAAI
OAAI分享收藏
查看更多内容